Exploring the effect of fingertip aero-haptic feedforward cues in directing eyes-free target acquisition in VR

Background The sense of touch plays a crucial role in interactive behavior within virtual spaces,particularly when visual attention is absent.Although haptic feedback has been widely used to compensate for the lack of visual cues,the use of tactile information as a predictive feedforward cue to guide hand movements remains unexplored and lacks theoretical understanding.Methods This study introduces a fingertip aero-haptic rendering method to investigate its effectiveness in directing hand movements during eyes-free spatial interactions.The wearable device incorporates a multichannel micro-airflow chamber to deliver adjustable tactile effects on the fingertips.Results The first study verified that tactile directional feedforward cues significantly improve user capabilities in eyes-free target acquisition and that users rely heavily on haptic indications rather than spatial memory to control their hands.A subsequent study examined the impact of enriched tactile feedforward cues on assisting users in determining precise target positions during eyes-free interactions,and assessed the required learning efforts.Conclusions The haptic feedforward effect holds great practical promise in eyeless design for virtual reality.We aim to integrate cognitive models and tactile feedforward cues in the future,and apply richer tactile feedforward information to alleviate users'perceptual deficiencies.

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

Research on Anti-Fluctuation Control of Winding Tension System Based on Feedforward Compensation

In the fiber winding process,strong disturbance,uncertainty,strong coupling,and fiber friction complicate the winding constant tension control.In order to effectively reduce the influence of these problems on the tension output,this paper proposed a tension fluctuation rejection strategy based on feedforward compensation.In addition to the bias harmonic curve of the unknown state,the tension fluctuation also contains the influence of bounded noise.A tension fluctuation observer(TFO)is designed to cancel the uncertain periodic signal,in which the frequency generator is used to estimate the critical parameter information.Then,the fluctuation signal is reconstructed by a third-order auxiliary filter.The estimated signal feedforward compensates for the actual tension fluctuation.Furthermore,a time-varying parameters fractional-order PID controller(TPFOPID)is realized to attenuate the bounded noise in the fluctuation.Finally,TPFOPID is enhanced by TFO and applied to control a tension control system considering multi-source disturbances.The stability of the method is analyzed by using the Lyapunov theorem.Finally,numerical simulations verify that the proposed scheme improves the tracking ability and robustness of the system in response to tension fluctuations.

Laboratory Implementation of Direct Torque Controller based Speed Loop Pseudo Derivative Feedforward Controller for PMSM Drive

This paper,evaluate the effectiveness of a proposed speed loop pseudo derivative feedforward(PDFF)controller-based direct torque controller(DTC)for a PMSM drive against the performance of existing PI speed controller-based DTC and hysteresis current controller(HCC).The proposed PDFF-based speed regulator effectively reduces oscillation and overshoot associated with rotor angular speed,electromagnetic torque,and stator current.Two case studies,one using forward-to-reverse motoring operation and the other involving reverse-to-forward braking operation,has been validated to show the effectiveness of the proposed control strategy.The proposed controller's superior performance is demonstrated through experimental verification utilizing an FPGA controller for a 1.5 kW PMSM drive laboratory prototype.

Application Effect of Feedforward Control in Outpatient Blood Specimen Management

Objective:To analyze the application effect of feedforward control in outpatient blood specimen management.Methods:1,200 patients who had their venous blood collected in outpatient phlebotomy room of our hospital's outpatient clinic from January 2021 to April 2021 were selected as study subjects and divided into 600 cases in the control group and 600 cases in the observation group.The two groups of patients were compared in terms of their satisfaction with the staff,the efficiency of the nurses and the quality of nursing care,turnaround time before specimen analysis,the rejection rate of the blood specimens,and the time of result reporting.Results:After the implementation of feedforward control,patients'satisfaction with staff,nurses work efficiency and quality of care,turnaround time before specimen analysis,specimen rejection rate,and result reporting time in the observation group were significantly higher than those in the control group(P<0.05).Conclusion:The application of feedforward control in the management of outpatient blood specimens has significant effect,which effectively improves patients'satisfaction,enhances the efficiency of nurses and the quality of nursing care,shortens the turnaround time of specimens before analysis and the reporting time of results,and reduces the rejection rate of specimens.

Matrix pencil based robust control for feedforward systems with event-triggered communications and sensor/actuator faults

In this paper we address the issue of output-feedback robust control for a class of feedforward nonlinear systems.Essentially different from the related literature,the feedback/input signals are corrupted by additive noises and can only be transmitted intermittently due to the consideration of event-triggered communications,which bring new challenges to the control design.With the aid of matrix pencil based design procedures,regulating the output to near zero is globally solved by a non-conservative dynamic low-gain controller which requires only an a priori information on the upper-bound of the growth rate of nonlinearities.Theoretical analysis shows that the closed-loop system is input-to-state stable with respect to the sampled errors and additive noise.In particular,the observer and controller designs have a dual architecture with a single dynamic scaling parameter whose update law can be obtained by calculating the generalized eigenvalues of matrix pencils offline,which has an advantage in the sense of improving the system convergence rate.

An Improved Time Feedforward Connections Recurrent Neural Networks

Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.

单相级联型多电平变换器直流纹波电压分析及抑制策略

级联H桥型多电平变换器采用移相变压器+整流供电时具有功率因数高、输出电压谐波含量低等优点,在大功率工业领域范围内得到了广泛应用。传统的调制算法都以假设直流母线电压恒定为前提,然而在直流侧会出现低频纹波并被调制和传输到负载,导致输出电压波形畸变。目的为了探讨级联H桥单元供电侧存在大量直流纹波电压导致输出负载电压波形畸变的问题,方法本文首先采用双重傅里叶理论分析直流纹波电压,在此基础上引入一种直流纹波前馈补偿策略,通过测量每个级联H桥的直流侧实际输入电压除以电压基准值并与传统载波信号相乘,进而与调制电压进行载波移相调制;同时采用矢量控制,通过FFT变换虚拟出无功电压,以获得更快的收敛速度、更高的稳定性和更小的振荡。结果仿真和实验结果表明,所提调制算法和控制策略可有效抑制负载电压中的低次谐波,减小输出电压波形畸变,实现逆变电源的快速动态响应和无稳态误差控制。结论实验结果验证了本文所采用的调制策略和控制策略的可行性与合理性。

基于角频率暂态前馈的VSG控制策略

针对虚拟同步发电机(VSG)并网时,输出功率的稳态性能与动态性能之间存在耦合,无法同时满足抑制有功振荡和电网一次调频的需求,提出基于角频率暂态前馈的控制策略,通过补偿输出功率,减小Δω,进而减小振荡过程。该控制策略可以有效抑制有功振荡,并且实现了稳态性能与动态性能间的解耦,不会影响一次调频特性,同时提高了响应速度。首先建立VSG有功闭环小信号模型,分析了输出功率稳态性能与动态性能之间的矛盾,并根据同步发电机功角特性分析了有功振荡机理;在此基础上提出了基于角频率暂态前馈的控制策略,利用闭环零极点与Bode图分析了所提控制策略对系统的影响;最后通过仿真和RT-LAB半实物平台验证了所提控制策略的可行性与优越性。

混沌自适应非洲秃鹫优化算法训练多层感知器

针对训练多层感知器(MLP)时,算法对初始值敏感、易陷入局部最优和收敛速度慢等问题,对新型启发式算法非洲秃鹫优化算法提出改进算法IAVOA。在初始化种群时引入Logistic混沌映射,增加种群的多样性;对最优秃鹫和次优秃鹫增加自适应权重系数,自动调整这两类秃鹫对普通秃鹫的引导作用;IAVOA用于MLP的训练,采用均方误差的平均值作为适应度函数寻找MLP的连接权重和偏差的最佳组合。选取4个不同复杂度的分类数据集,比较IAVOA算法与现有启发式算法对MLP训练后,MLP对数据分类的性能,仿真结果表明,IAVOA算法训练的MLP在数据分类准确率、全局搜索能力、收敛速度和稳定性方面均具有良好的性能。

基于幂指数法的地震模拟振动台频域前馈补偿方法

针对地震模拟振动台受到系统和试件非线性等因素影响,导致波形再现精度不高的问题,提出基于幂指数法的地震模拟振动台频域前馈补偿方法以提高振动台的控制效果,通过数值仿真和实际振动台试验对该方法的控制性能进行研究。研究结果表明:基于幂指数法的地震模拟振动台频域前馈补偿方法能够快速收敛,迭代次数较少,可以大幅提高振动台的波形复现精度,能够提高振动台的控制性能。

基于前馈—模糊PID策略的风洞控制器应用

针对2.4m跨声速风洞因强耦合、强非线性、大滞后等特性无法获得其精确数学模型,使用经典PID算法控制精度无法满足试验要求的问题,设计基于前馈—模糊PID策略的复合控制器,并应用于风洞试验。试验结果表明,该控制器的应用对于流场控制精度提高有良好的效果。

无线传感节点中频域传输信号解调与控制方法

为提高无线传感网络节点中的频域传输信号抗噪能力,优化前端镜像与噪声信号处理能力,以无线传感节点中的频域传输信号为研究对象,提出一种新的信号控制与解调方法。将无线传感信号输入设计的滤波器中,通过电容器、电阻、运算放大器相互协作完成信号滤波。引入前馈补偿通路组建两级节点信号解调线路,当输出传感信号功率超过标准,便可根据超出值,以前馈补偿的方式调节增益带宽积实现解调。在传感节点中外接一个校正电路,以校正电容调节码的方式,控制传感信号频域传输特性满足正常运行标准。仿真结果表明:在1 V的电源电压下,此滤波器中心频率为2 MHz,邻近信号抑制为55 dB,镜像信号抑制为33 dB,带内纹波为0.1 dB,通带电压增益为18 dB,工作电流为382μA。验证了所提方法具有较高的频域传输信号深度处理能力,且有助于实现无线传感网络高质量通信。

基于深度学习的可见光通信系统室内三维定位

针对目前室内可见光通信系统三维定位的准确率与定位速度依然不佳的问题,提出一种基于深度学习的可见光通信系统室内定位方法。首先,设计了一个神经网络将指纹数据编码成二维阵列,利用卷积神经网络学习指纹阵列与目标位置之间的关系;然后,通过粒子群优化算法自动搜索卷积神经网络的超参数,以降低深度神经网络的训练难度。此外,设计了定位数据训练集、验证集与测试集的划分方法,有助于缓解神经网络的过拟合问题,并提高定位准确性。仿真结果表明,所提方法在6×6×4m3室内环境下的平均定位误差为0.024m,平均定位时间为0.478s。

一种适应卫星姿态侧摆机动的混合轮系控制方法

针对高稳定度轮控大型卫星因动量轮力矩小而难以满足侧摆成像任务需求问题,开展了混合轮系配置方案及控制方法研究。首先,提出常规轮系配置卫星滚动轴方向串联大力矩飞轮的混合配置方案,并设计飞轮开环前馈与常规动量轮闭环反馈相结合的控制策略;其次,设计以容许角动量最大量为权重的飞轮组前馈力矩分配律和具有角动量反馈的飞轮力矩跟踪控制方法,实现飞轮力矩合理分配及其模型不确定性导致输出力矩偏差抑制;然后,设计估计器对星体内外扰动力矩进行估计与补偿,以确保卫星高稳定度性能实现;最后,给出在轨应用情况,验证所提出方案及方法的有效性。在轨应用表明,采用该控制方法,有效缩短了整星侧摆稳定时间达100 s以上,且非机动期间姿态稳定度优于3.75×10-5(°)/s。

实现纹波电流还原的PMSM端口模拟算法

电机模拟器作为一种模拟电机端口特性的三相电力电子装置,为电驱动系统的测试提供了高效的测试手段。电机模拟器还原目标电机端口特性的标志是精确还原目标电机的工作电流,现有研究实现了基波电流及较低阶次谐波电流的还原,但针对电机在电机控制器驱动下的高频纹波电流的还原仍严重依赖滤波电感与目标电机电感的匹配,降低了电机模拟器的通用性。为此,本文基于电路等效虚拟法将永磁同步电机等效电路拆分为两部分,一部分由电机模拟器实际电路代替,另一部分由控制算法模拟,并结合无差拍电流预测控制对控制差拍进行了补偿,提出了前馈解耦无差拍电流跟随策略。实验结果表明,基于新提出的永磁同步电机端口模拟算法,电机模拟器可以在不更换电感的情况下模拟不同参数的永磁同步电机,高频纹波电流的频域跟踪误差从传统策略的160%降低至20%,显著提高了电机模拟器对永磁同步电机端口特性的模拟精度。

内力加载机构基于模型前馈的多电机同步控制方法

针对某圆筒形分体式构件对心加载的需求,设计了一种四电机驱动的内力加载机构,可向结构件施加超过15 000 N的总加载力,但实际加载过程中由于结构不对称等原因,易造成加载偏载,结构件之间发生相对移动,影响装配质量。为此,提出了一种基于模型前馈的比例-微分(PD)控制方法用于多电机的同步运动控制,建立了交流伺服电机的给定信号与角速度之间的动力学模型,基于该模型的目标速度设计了前馈补偿,以增强多电机同步控制时的响应速度。同时,设计了PD反馈控制参数,以提高同步控制的精度。试验结果表明,与传统的交叉耦合同步算比较,基于模型的PD同步控制算法同步误差减少了约40%,电机同步性得到了有效提高。

燃料电池系统建模与供气系统控制方法

为了研究燃料电池系统的特性以及空气系统对燃料电池特性的影响,利用Simulink仿真软件对燃料电池系统进行建模,模型包括电压模型、空压机模型、阴极模型、阳极模型.分别通过前馈比例-积分-微分(proportional-integral-differential, PID)和模糊PID对燃料电池空气系统进行控制.结果表明:在负载电流变化时,前馈PID和模糊PID都能够使过氧比达到设定的常数值2.0附近,但模糊PID比前馈PID响应更快,且模糊PID控制下的燃料电池输出功率波动较小,燃料电池系统更加稳定.

基于直驱并联机构的六自由度振动校准系统研究

直驱并联机构六自由度振动校准系统可以实现高精度和多自由度的运动模拟和校准,具有良好的应用前景。为了解决平台驱动关节滑块位置跟踪精度低的问题,对该机构进行动力学力矩前馈补偿分析。利用虚功原理建立该机构动力学模型,提出一种基本伺服算法与动力学前馈补偿相结合的控制策略。在实验样机上进行力矩前馈补偿,实验结果表明,在开放伺服程序增加力矩前馈补偿后,可以降低该机构在运动时的跟随误差,进行不同幅值单自由度正弦运动时,跟随误差分别下降40.32%、39.04%、43.24%、48.19%。并对振动台进行了性能检测,在平台基础上,搭建激光测量系统和数据采集模块,进行传感器校准和性能分析,平台横向振动比较小,谐波失真度均小于2%,平台具有较好的稳定性和性能。

前馈控制理论护理在乙型肝炎肝硬化患者遵医行为及生存质量中的应用效果

目的探究前馈控制理论护理对乙型肝炎肝硬化患者遵医行为及生存质量的应用效果。方法选取2022年2月至2023年10月南通大学附属南通第三医院的110例乙型肝炎肝硬化患者作为研究对象,按照随机数字表法将其分为对照组和观察组,各55例。对照组采用常规护理,观察组采用前馈控制理论护理,干预2个月后比较两组遵医行为、心理状态评分及生存质量。结果干预后,两组遵医行为评分指标高于干预前,且观察组高于对照组,差异有统计学意义(P<0.05);干预后,两组90项症状自评量表评分低于干预前,且观察组低于对照组,差异有统计学意义(P<0.05)。干预后,两组肝病生存质量调查表评分高于干预前,且观察组高于对照组,差异有统计学意义(P<0.05)。结论乙型肝炎肝硬化患者护理中接受前馈控制护理,提高遵医行为,改善患者心理状态的同时,患者生存质量有所提升,预后效果更佳,可广泛应用于临床中。